Transformer architectures have revolutionized the field of natural language processing (NLP) due to their outstanding ability to capture long-range dependencies within text. Unlike traditional recurrent neural networks (RNNs), which process information sequentially, transformers leverage a mechanism called self-attention to weigh the relevance of e

Transformers have emerged as a groundbreaking paradigm in the field of natural language processing (NLP). These architectures leverage attention mechanisms to process and understand data in an unprecedented fashion. With their skill to capture extended dependencies within sequences, transformers have achieved state-of-the-art accuracy on a wide ran